What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors?

Kim, Hyeongwoo and Son, Jisoo (2023): What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors?

Abstract

Charge-offs signal important information about the riskiness of loan portfolios in the banking system, which can generate systemic risk towards deep recessions. We compiled the net charge-off rate (COR) data of the top 10 bank holding companies (BHCs) in the U.S., utilizing consolidated financial statements. We propose factor-augmented forecasting models for CORs by estimating latent common factors, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models outperform the benchmark models especially well for business loan and real estate loan CORs, while enhancing predictive contents for consumer loan CORs is difficult especially at short horizons. Real activity factors improve the out-of-sample predictability over the benchmarks for business loan CORs even when financial sector factors are excluded.

• Item Type: MPRA Paper
• Language: English
• Keywords: Net Charge-Off Rate; Bank Holding Companies; Principal Component Analysis; Partial Least Squares; Out-of-Sample Forecast
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; F - International Economics > F3 - International Finance

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